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Found 8 papers, 2 papers with code
Concept-Based Explainable Artificial Intelligence: Metrics and Benchmarks
Concept-based explanation methods, such as concept bottleneck models (CBMs), aim to improve the interpretability of machine learning models by linking their decisions to human-understandable concepts, under the critical assumption that such concepts can be accurately attributed to the network's feature space.
Revisiting Cross-Domain Problem for LiDAR-based 3D Object Detection
Deep learning models such as convolutional neural networks and transformers have been widely applied to solve 3D object detection problems in the domain of autonomous driving.
Penny-Wise and Pound-Foolish in Deepfake Detection
This paper critiques the overly specialized approach of fine-tuning pre-trained models solely with a penny-wise objective on a single deepfake dataset, while disregarding the pound-wise balance for generalization and knowledge retention.
Detect Closer Surfaces that can be Seen: New Modeling and Evaluation in Cross-domain 3D Object Detection
This raises a fundamental question related to the evaluation of the 3D object detection models' cross-domain performance: Do we really need models to maintain excellent performance in their original 3D bounding boxes after being applied across domains?
Orthogonalising gradients to speedup neural network optimisation
The optimisation of neural networks can be sped up by orthogonalising the gradients before the optimisation step, ensuring the diversification of the learned representations.
Generalisation and the Geometry of Class Separability
Recent results in deep learning show that considering only the capacity of machines does not adequately explain the generalisation performance we can observe.
Linear Disentangled Representations and Unsupervised Action Estimation
In this work we empirically show that linear disentangled representations are not generally present in standard VAE models and that they instead require altering the loss landscape to induce them.
Extended Formulations for Online Linear Bandit Optimization
On-line linear optimization on combinatorial action sets (d-dimensional actions) with bandit feedback, is known to have complexity in the order of the dimension of the problem.
